
Model-1 simply concatenates \(Q\)  and \(\mathcal {H}\)  as input to a standard LSTM-based s2s model.

Model-2 uses two separate encoders to encode \(Q\)  and \(\mathcal {H}\) , respectively, and then uses a two-layer attention mechanism to fuse \(Q\)  and \(\mathcal {H}\)  into the decoder to generate \(\hat{Q}\) . The model is illustrated in Figure REF .

Model-3 fuses \(\mathcal {H}\)  into \(Q\)  during encoding using an attention mechanism, then feeds the context-embedded representation of \(Q\)  into the decoder to generate \(\hat{Q}\) .

Model-4 combines Model-2 and Model-3, and is the best performer among the four models as reported in [1]}.

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